The first High-Level Information Fusion (HLIF) competition took place in 2024, proposing a challenge for supporting aircraft pilots handling “Notices to Air Missions”. Technical difficulties on both the input datasets and the competition architecture raised the bar to submissions. Only two solutions taking the form of self-contained software were submitted, and were compared with two references produced by the organizers. A detailed analysis shows that some misunderstanding of the underlying use-case objectives had a big impact on the choices made by the participants. Those misunderstandings were due to a lack of precisions in the problem descriptions, along with differences in contestants' backgrounds. Consequently, the competition cannot rank the available solutions. However, our analysis highlights some interesting epistemological aspects of such a setting, related to the design and use of ontologies, knowledge graphs and queries. This allows us to propose a set of advices for improving future occurrences of an HLIF competition, on the same use-case or on another one.

A widely accepted way to assess the performance of iterative black-box optimizers is to analyze their empirical cumulative distribution function (ECDF) of predefined quality targets achieved not later than a given runtime. In this work, we consider an alternative approach, based on the empirical attainment function (EAF) and we show that the target-based ECDF is an approximation of the EAF. We argue that the EAF has several advantages over the target-based ECDF. In particular, it does not require defining a priori quality targets per function, captures performance differences more precisely, and enables the use of additional summary statistics that enrich the analysis. We also show that the average area over the convergence curves is a simpler-to-calculate, but equivalent, measure of anytime performance. To facilitate the accessibility of the EAF, we integrate a module to compute it into the IOHanalyzer platform. Finally, we illustrate the use of the EAF via synthetic examples and via the data available for the black-box optimization benchmark suite.

Large-scale high-level information fusion and data integration is a pressing need in several scientific domains. Recently, the biomedical community established BioCypher, a tool to help create large Semantic Knowledge Graphs (SKGs) in a simple and reproducible way. In this article, we introduce OntoWeaver, a companion tool to BioCypher that allows to easily extract tabular data into SKGs by using a simple declarative mapping. OntoWeaver allows implementing reproducible mappings from tabular data to SKGs with a simple declarative configuration. The use of OntoWeaver and BioCypher is demonstrated in two different use cases: cancer database integration and invasive species monitoring. We believe that OntoWeaver and BioCypher, both free and open-source software, can help several scientific communities working on SKGs and high-level information fusion problems.

With the rapidly growing use of Multi-Agent Systems (MASs), which can exponentially increase the system complexity, the problem of planning a mission for MASs became more intricate. In some MASs, human operators are still involved in various decision-making processes, including manual mission planning, which can be an ineffective approach for any non-trivial problem. Mission planning and re-planning can be represented as a combinatorial optimization problem. Computing a solution to these types of problems is notoriously difficult and not scalable, posing a challenge even to cutting-edge solvers. As time is usually considered an essential resource in MASs, automated solvers have a limited time to provide a solution. The downside of this approach is that it can take a substantial amount of time for the automated solver to provide a sub-optimal solution. In this work, we are interested in the interplay between a human operator and an automated solver and whether it is more efficient to let a human or an automated solver handle the planning and re-planning problems, or if the combination of the two is a better approach. We thus propose an experimental setup to evaluate the effect of having a human operator included in the mission planning and re-planning process. Our tests are performed on a series of instances with gradually increasing complexity and involve a group of human operators and a metaheuristic solver based on a genetic algorithm. We measure the effect of the interplay on both the quality and structure of the output solutions. Our results show that the best setup is to let the operator come up with a few solutions, before letting the solver improve them.

The configuration of radar networks is a complex problem that is often performed manually by experts with the help of a simulator. Different numbers and types of radars as well as different locations that the radars shall cover give rise to different instances of the radar configuration problem. The exact modeling of these instances is complex, as the quality of the configurations depends on a large number of parameters, on internal radar processing, and on the terrains on which the radars need to be placed. Classic optimization algorithms can therefore not be applied to this problem, and we rely on "trial-and-error" black-box approaches.

Per Instance Algorithm Configuration (PIAC) relies on features that describe problem instances. It builds an Empirical Performance Model (EPM) from a training set made of (instance, parameter configuration) pairs together with the corresponding performance of the algorithm at hand. This paper presents a case study in the continuous black-box optimization domain, using features proposed in the literature. The target algorithm is CMA-ES, and three of its hyper-parameters. Special care is taken to the computational cost of the features. The EPM is learned on the BBOB benchmark, but tested on independent test functions gathered from the optimization literature. The results demonstrate that the proposed approach can outperform the default setting of CMA-ES with as few as 30 or 50 time the problem dimension additional function evaluations for feature computation.

Multicore machines are becoming a standard way to speed up the system performance. After having instantiated the evolutionary metaheuristic DAEX with the forward search YAHSP planner, we investigate on the global parallelism approach, which exploits the intrinsic parallelism of the individual evaluation. This paper describes a parallel shared-memory version of the DAEYAHSP planning system using the OpenMP directive-based API. The parallelization scheme applies at a high level of abstraction and thus can be used by any evolutionary algorithm implemented with the Evolving Objects framework. The proof of concept is validated on a 48-core machine with two planning tasks extracted from the last international planning competition. Experiments show significant speedups with an increasing number of cores. This preliminary work opens an avenue for parallelizing any evolutionary algorithm developed with EO that would target multicore architectures.